Postmortem studies of the human brain reveal consistent age-related reductions in brain size and age-related increases in cerebro-spinal fluid spaces (CSF) which are accentuated in Alzheimer's disease. Previous studies utilizing quantitative X-ray computerized axial tomography (CT) have confirmed these postmortem findings in healthy controls free of obvious brain disease. Quantitative CT also has demonstrated significant, progressive increase in ventricular CSF size accompanying development of dementia of the Alzheimer type. Quantitative CT, however, has several limitations which prevent accurate determination of subarachnoid CSF and of temporal lobe volumes, such as bone hardening artifact and lack of high resolution coronal imaging.
Magnetic resonance imaging (MRI) offers high contrast images which are unimpaired by bone-hardening artifacts intrinsic to CT. In addition, flexible imaging sequences and choices of image orientation allow for detailed analysis of the temporal lobes, and for computer methods which can accurately segment the brain into CSF, gray matter and white matter compartments.
To date, a number of quantitative MRI methods have reported measurements of CSF, temporal lobe, gray and white matter volumes in brain. Three general methods have been employed: (1) operator directed outlining of a region of interest (ROI); (2) special sequences to enhance CSF and suppress brain matter signals; and (3) segmentation routines which utilize either automatic boundary outlining or threshold determinations.
Although tracing a ROI can be quick and simple, it is the most operator intensive, and requires extensive training as well as a detailed knowledge of neuroanatomy. Special MRI sequences have been designed to selectively enhance the CSF signal for volume determination, but it is not clear how partial volume averaging is accounted for with those sequences. Moreover, the MRI images generated are not suitable for standard radiological interpretation. Automatic boundary outlining or threshold segmentation routines are usually very time consuming, and require the operator to select "seed" pixel values to start the outlining, or to sample representative pixel intensity values for brain and CSF segmentation.
The present invention provides a simple method of automatically determining an accurate threshold for separating CSF from brain matter signals utilizing any of several T1 weighted MRI images.